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softmax does cost now, special case 1x1 convs

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This commit is contained in:
Joseph Redmon 2018-05-04 01:28:59 -07:00
parent 508381b37f
commit e7405b513d
21 changed files with 41 additions and 65 deletions
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4
Makefile
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@ -68,8 +68,8 @@ EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
OBJS = $(addprefix $(OBJDIR), $(OBJ))
DEPS = $(wildcard src/*.h) Makefile include/darknet.h
#all: obj backup results $(SLIB) $(ALIB) $(EXEC)
all: obj results $(SLIB) $(ALIB) $(EXEC)
all: obj backup results $(SLIB) $(ALIB) $(EXEC)
#all: obj results $(SLIB) $(ALIB) $(EXEC)
$(EXEC): $(EXECOBJ) $(ALIB)

3
cfg/alexnet.cfg
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@ -90,6 +90,3 @@ activation=linear
[softmax]
groups=1
[cost]
type=sse

3
cfg/cifar.cfg
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@ -119,6 +119,3 @@ activation=leaky
[softmax]
groups=1
[cost]

2
cfg/cifar.test.cfg
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@ -115,5 +115,3 @@ activation=leaky
groups=1
temperature=3
[cost]

3
cfg/darknet19.cfg
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@ -203,6 +203,3 @@ activation=linear
[softmax]
groups=1
[cost]
type=sse

3
cfg/darknet19_448.cfg
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@ -195,6 +195,3 @@ activation=linear
[softmax]
groups=1
[cost]
type=sse

3
cfg/densenet201.cfg
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@ -1949,6 +1949,3 @@ activation=linear
[softmax]
groups=1
[cost]
type=sse

3
cfg/extraction.cfg
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@ -201,6 +201,3 @@ activation=leaky
[softmax]
groups=1
[cost]
type=sse

3
cfg/extraction22k.cfg
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@ -204,6 +204,3 @@ activation=leaky
[softmax]
groups=1
[cost]
type=sse

3
cfg/go.cfg
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@ -130,6 +130,3 @@ stride=1
[softmax]
[cost]
type=sse

2
cfg/go.test.cfg
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@ -129,6 +129,4 @@ stride=1
[softmax]
[cost]
type=sse

2
cfg/gru.cfg
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@ -27,6 +27,4 @@ activation=linear
[softmax]
[cost]
type=sse

3
cfg/resnet152.cfg
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@ -1458,6 +1458,3 @@ activation=linear
[softmax]
groups=1
[cost]
type=sse

2
cfg/resnet50.cfg
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@ -506,6 +506,4 @@ activation=linear
[softmax]
groups=1
[cost]
type=sse

2
cfg/rnn.cfg
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@ -35,6 +35,4 @@ activation=leaky
[softmax]
[cost]
type=sse

2
cfg/rnn.train.cfg
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@ -35,6 +35,4 @@ activation=leaky
[softmax]
[cost]
type=sse

3
cfg/strided.cfg
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@ -180,6 +180,3 @@ activation=ramp
[softmax]
[cost]
type=sse

2
cfg/tiny.cfg
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@ -171,6 +171,4 @@ activation=linear
[softmax]
groups=1
[cost]
type=sse

2
cfg/vgg-16.cfg
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@ -148,6 +148,4 @@ activation=linear
[softmax]
groups=1
[cost]
type=sse

26
src/convolutional_kernels.cu
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@ -111,9 +111,13 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network net)
float *a = l.weights_gpu + j*l.nweights/l.groups;
float *b = net.workspace;
float *c = l.output_gpu + (i*l.groups + j)*n*m;
float *im = net.input_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
im2col_gpu(net.input_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w,
l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
if (l.size == 1){
b = im;
} else {
im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
}
gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
}
@ -236,22 +240,26 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network net)
float *b = net.workspace;
float *c = l.weight_updates_gpu + j*l.nweights/l.groups;
float *im = net.input_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
float *im = net.input_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
float *imd = net.delta_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
im2col_gpu(im, l.c/l.groups, l.h, l.w,
l.size, l.stride, l.pad, b);
im2col_gpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
if(net.delta_gpu){
if(l.binary || l.xnor) swap_binary(&l);
if (net.delta_gpu) {
if (l.binary || l.xnor) swap_binary(&l);
a = l.weights_gpu + j*l.nweights/l.groups;
b = l.delta_gpu + (i*l.groups + j)*m*k;
c = net.workspace;
if (l.size == 1) {
c = imd;
}
gemm_gpu(1,0,n,k,m,1,a,n,b,k,0,c,k);
col2im_gpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride,
l.pad, net.delta_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w);
if (l.size != 1) {
col2im_gpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
}
if(l.binary || l.xnor) {
swap_binary(&l);
}

30
src/convolutional_layer.c
View File

@ -463,9 +463,13 @@ void forward_convolutional_layer(convolutional_layer l, network net)
float *a = l.weights + j*l.nweights/l.groups;
float *b = net.workspace;
float *c = l.output + (i*l.groups + j)*n*m;
float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
im2col_cpu(net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w,
l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
if (l.size == 1) {
b = im;
} else {
im2col_cpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
}
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
}
@ -501,21 +505,31 @@ void backward_convolutional_layer(convolutional_layer l, network net)
float *b = net.workspace;
float *c = l.weight_updates + j*l.nweights/l.groups;
float *im = net.input+(i*l.groups + j)*l.c/l.groups*l.h*l.w;
float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
float *imd = net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
if(l.size == 1){
b = im;
} else {
im2col_cpu(im, l.c/l.groups, l.h, l.w,
l.size, l.stride, l.pad, b);
}
im2col_cpu(im, l.c/l.groups, l.h, l.w,
l.size, l.stride, l.pad, b);
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
if(net.delta){
if (net.delta) {
a = l.weights + j*l.nweights/l.groups;
b = l.delta + (i*l.groups + j)*m*k;
c = net.workspace;
if (l.size == 1) {
c = imd;
}
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride,
l.pad, net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w);
if (l.size != 1) {
col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
}
}
}
}